Browsing by Author "Yeh, Chung-Hsuan"

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Driving behaviour-based event data recorder
(2014-06-01)
Wu, Bing-Fei
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Chen, Ying-Han
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Yeh, Chung-Hsuan
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電機工程學系
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Department of Electrical and Computer Engineering
A general event data recorder is a device installed in automobiles to record information related to vehicle crashes or accidents. The data provide a better understanding of how certain crashes come about. This study made a prototype of a driving behaviour-based event data recorder (DBEDR), which provides the information of driving behaviours and a danger level. The authors approach is to recognise the seven behaviours: normal driving, acceleration, deceleration, changing to the left lane or right lane, zigzag driving and approaching the car in front by the hidden Markov models. All data were collected from a real vehicle and evaluated in a real road environment. The experimental results show that the proposed method achieved an average detection ratio of 95% for behaviour recognition. The danger level is inferred by fuzzy rules involved with the above behaviours. DBEDR recorded the recognised driving behaviours and the danger level, and the places were stored with the assistance of a global positioning system receiver. By integrating Google Maps, the locations, the driving behaviour occurrences, the danger level on the travel routes and the recorded images, the proposed DBEDR could be more useful compared with the traditional EDRs.
Fuzzy Logic Based Driving Behavior Monitoring using Hidden Markov Models
(2012-01-01)
Wu, Bing-Fei
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Chen, Ying-Han
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Yeh, Chung-Hsuan
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
This paper proposes a driving behavior monitoring system to provide drivers an indicator of the danger level for driving safety. The major challenge of the research is to label whether a pattern is dangerous or not for training data. Our approach is to recognize the seven behaviors, normal driving, acceleration, deceleration, changing to the left lane or right lane, zigzag driving, and approaching the car in front by hidden Markov models. The danger-level indicator, on behalf of the dangerous situation of drivers, is inferred by fuzzy rules involved with the above behaviors. The higher the value represents the worse status of drivers, and it also provides three colors representing different levels of warnings to remind drivers of their own states. The uncertain definition of a dangerous pattern is avoided, and the related behaviors leading to the current danger level are offered instead. Moreover, unlike many studies using simulators to validate their systems and experimental results, we collected data from a real vehicle and evaluated the proposed system in a real road environment. The experimental results show that the proposed method achieved an average detection ratio of 95% for behavior recognition and can be used for driving safety.
Reasoning-Based Framework for Driving Safety Monitoring Using Driving Event Recognition
(2013-09-01)
Wu, Bing-Fei
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Chen, Ying-Han
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Yeh, Chung-Hsuan
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Li, Yen-Feng
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電機工程學系
With the growing concern for driving safety, many driving-assistance systems have been developed. In this paper, we develop a reasoning-based framework for the monitoring of driving safety. The main objective is to present drivers with an intuitively understood green/yellow/red indicator of their danger level. Because the danger levelmay change owing to the interaction of the host vehicle and the environment, the proposed framework involves two stages of danger-level alerts. The first stage collects lane bias, the distance to the front car, longitudinal and lateral accelerations, and speed data from sensors installed in a real vehicle. All data were recorded in a normal driving environment for the training of hidden Markov models of driving events, including normal driving, acceleration, deceleration, changing to the left or right lanes, zigzag driving, and approaching the car in front. In addition to recognizing these driving events, the degree of each event is estimated according to its character. In the second stage, the danger-level indicator, which warns the driver of a dangerous situation, is inferred by fuzzy logic rules that address the recognized driving events and their degrees. A hierarchical decision strategy is also designed to reduce the number of rules that are triggered. The proposed framework was successfully implemented on a TI DM3730-based embedded platform and was fully evaluated in a real road environment. The experimental results achieved a detection ratio of 99% for event recognition, compared with that achieved by four conventional methods.